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Like Pythia, but 1234 isn't your favorite random seed? We retrained Pythia 9x using different random seeds to explore how stable analyses of learning dynamics are to randomness. Meet Pietro Lesci @blancheminerva Fri 1500-1730 Hall 3 + Hall 2B #259 x.com/pietro_lesci/s…

Happy to see our paper win ✨the Best Theme Paper Award✨ at #NAACL2025 ! Working on this project with this team was a lot of fun :-) huge thanks to Genta Winata, Frederikus Hudi,Patrick Amadeus,@davidanugraha, Rifki Afina Putri for leading!

The call for papers for the 8th FEVERworkshop at #ACL is out: fever.ai/workshop.html Deadline for is on May 19th! And if you have a paper already reviewed in ARR, you can commit it until June 9th!

📢 ACL 2025 notifications have been sent out, making this the perfect time to finalize your commitment. Don't miss the opportunity to be part of the workshop! 🔗 Commit here: openreview.net/group?id=aclwe… 🗓️ Deadline: May 20, 2025 (AoE) #ACL2025 #NLProc

If you're finishing your camera-ready for ACL (#acl2025nlp) or ICML (#icml2025 ) and want to cite co-first authors more fairly, I just made a simple fix to do this! Just add $^*$ to the authors' names in your bibtex, and the citations should change :) github.com/tpimentelms/ac…

A string may get 17 times less probability if tokenised as two symbols (e.g., ⟨he, llo⟩) than as one (e.g., ⟨hello⟩)—by an LM trained from scratch in each situation! Our #acl2025nlp paper proposes an observational method to estimate this causal effect! Longer thread soon!

If you use LLMs, tokenisation bias probably affects you: * Text generation: tokenisation bias ⇒ length bias 🤯 * Psycholinguistics: tokenisation bias ⇒ systematically biased surprisal estimates 🫠 * Interpretability: tokenisation bias ⇒ biased logits 🤔

Looking forward to this year's edition! With great speakers: Ryan McDonald Yulan He Vlad Niculae Antonis Anastasopoulos Raquel Fernández Anna Rogers Preslav Nakov Mohit Bansal Eunsol Choi Marie-Catherine de Marneffe !